Work

Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares

Year: 2016

Type: conference-paper

Abstract: We propose an online tensor subspace tracking algorithm based on the CP decomposition exploiting the recursive least squares (RLS), dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). Numerical evaluations show that the proposed OLSTEC algorithm gives faster convergence per iteration comparing with the state-of-the-art online algorithms.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp.2016.7472131

Author: Hiroyuki Kasai

Institution: University of Electro-Communications

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Ministry of Internal Affairs and Communications

OpenAlex ID: W2283679166

FWCI: 3.793

Cites: 26

Cited by: 48

Related to: 10